Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

OPINION

The diversity of GABAergic neurons and neural communication elements

Nature Reviews Neuroscience volume 20, pages 563–572 (2019)Cite this article

Subjects

Abstract

The phenotypic diversity of cortical GABAergic neurons is probably necessary for their functional versatility in shaping the spatiotemporal dynamics of neural circuit operations underlying cognition. Deciphering the logic of this diversity requires comprehensive analysis of multi-modal cell features and a framework of neuronal identity that reflects biological mechanisms and principles. Recent high-throughput single-cell analyses have generated unprecedented data sets characterizing the transcriptomes, morphology and electrophysiology of interneurons. We posit that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures. This conceptual framework integrates multi-modal cell features, captures neuronal input–output properties fundamental to circuit operation and may advance understanding of the appropriate granularity of neuron types, towards a biologically grounded and operationally useful interneuron taxonomy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A work draft taxonomy of transcriptomic neuron types of the cortical GABAergic system.
Fig. 2: Transcriptional signatures of synaptic communication delineate cardinal GABAergic neuron types.
Fig. 3: A conceptual framework of neuronal identity.

Similar content being viewed by others

References

  1. Bota, M. & Swanson, L. W. The neuron classification problem. Brain Res. Rev. 56, 79–88 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Luo, L., Callaway, E. M. & Svoboda, K. Genetic dissection of neural circuits: a decade of progress. Neuron 98, 865 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roux, L. & Buzsaki, G. Tasks for inhibitory interneurons in intact brain circuits. Neuropharmacology 88, 10–23 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Huang, Z. J. Toward a genetic dissection of cortical circuits in the mouse. Neuron 83, 1284–1302 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klausberger, T. & Somogyi, P. Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations. Science 321, 53–57 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cajal, S. R. Nuevo concepto de la histología de los centros nerviosos. Rev. Cienc. Med. Barc. 18, 361–376 (1892).

    Google Scholar 

  9. Kepecs, A. & Fishell, G. Interneuron cell types are fit to function. Nature 505, 318–326 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ascoli, G. A. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. DeFelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hardcastle, K., Ganguli, S. & Giocomo, L. M. Cell types for our sense of location: where we are and where we are going. Nat. Neurosci. 20, 1474–1482 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLOS Biol. 16, e2006387 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9, 99–107 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Rudy, B. et al. Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71, 45–61 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gerashchenko, D. et al. Identification of a population of sleep-active cerebral cortex neurons. Proc. Natl Acad. Sci. USA 105, 10227–10232 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. He, M. et al. Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex. Neuron 91, 1228–1243 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Obermayer, J. et al. Prefrontal cortical ChAT-VIP interneurons provide local excitation by cholinergic synaptic transmission and control attention. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/461723v1 (2018).

  30. Tasic, B. Single cell transcriptomics in neuroscience: cell classification and beyond. Curr. Opin. Neurobiol. 50, 242–249 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Nord, A. S. et al. Genomic perspectives of transcriptional regulation in forebrain development. Neuron 85, 27–47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sussel, L. et al. Loss of Nkx2.1 homeobox gene function results in a ventral to dorsal molecular respecification within the basal telencephalon: evidence for a transformation of the pallidum into the striatum. Development 126, 3359–3370 (1999).

    Article  CAS  PubMed  Google Scholar 

  33. Lim, L. et al. Development and functional diversification of cortical interneurons. Neuron 100, 294–313 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sandberg, M. et al. Transcriptional networks controlled by NKX2-1 in the development of forebrain GABAergic neurons. Neuron 91, 1260–1275 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vogt, D. et al. Lhx6 directly regulates Arx and CXCR7 to determine cortical interneuron fate and laminar position. Neuron 82, 350–364 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gouwens, N. W. et al. Classification of electrophysiological and morphological types in mouse visual cortex. Nat. Neurosci. (in the press).

  37. Economo, M. N. et al. A platform for brain-wide imaging and reconstruction of individual neurons. eLife 5, e10566 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gong, H. et al. High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat. Commun. 7, 12142 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, X. et al. Genetic single neuron anatomy reveals fine granularity of cortical axo-axonic cells. Cell Rep. 26, 3145–3159 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Li, R. et al. Precise segmentation of densely interweaving neuron clusters using G-Cut. Nat. Commun. 10, 1549 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Yang, J. et al. FMST: an automatic neuron tracing method based on fast marching and minimum spanning tree. Neuroinformatics 17, 185–196 (2019).

    Article  PubMed  Google Scholar 

  42. Ascoli, G. A. & Wheeler, D. W. In search of a periodic table of the neurons: axonal-dendritic circuitry as the organizing principle: patterns of axons and dendrites within distinct anatomical parcels provide the blueprint for circuit-based neuronal classification. Bioessays 38, 969–976 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Klausberger, T. et al. Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature 421, 844–848 (2003).

    Article  CAS  PubMed  Google Scholar 

  45. Varga, C., Golshani, P. & Soltesz, I. Frequency-invariant temporal ordering of interneuronal discharges during hippocampal oscillations in awake mice. Proc. Natl Acad. Sci. USA 109, E2726–E2734 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34, 199–203 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Fuzik, J. et al. Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat. Biotechnol. 34, 175–183 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Paul, A., Huang, Z. J. & Single-cell, R. N. A. sequencing of fluorescently labeled mouse neurons using manual sorting and double in vitro transcription with absolute counts sequencing (DIVA-Seq). J. Vis. Exp. 140, e58690 (2018).

    Google Scholar 

  49. Hartwell, L. H. et al. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  PubMed  Google Scholar 

  50. Crow, M. et al. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Lerner, T. N., Ye, L. & Deisseroth, K. Communication in neural circuits: tools, opportunities, and challenges. Cell 164, 1136–1150 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. DeFelipe, J. et al. A microcolumnar structure of monkey cerebral cortex revealed by immunocytochemical studies of double bouquet cell axons. Neuroscience 37, 655–673 (1990).

    Article  CAS  PubMed  Google Scholar 

  53. Douglas, R. J. & Martin, K. A. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Szentagothai, J. The ‘module-concept’ in cerebral cortex architecture. Brain Res. 95, 475–496 (1975).

    Article  CAS  PubMed  Google Scholar 

  55. da Costa, N. M. & Martin, K. A. Whose cortical column would that be? Front. Neuroanat. 4, 16 (2010).

    PubMed  PubMed Central  Google Scholar 

  56. Sumbul, U. et al. Automated computation of arbor densities: a step toward identifying neuronal cell types. Front. Neuroanat. 8, 139 (2014).

    PubMed  PubMed Central  Google Scholar 

  57. Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Jonas, E. & Kording, K. Automatic discovery of cell types and microcircuitry from neural connectomics. eLife 4, e04250 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Pi, H. J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Mayer, C. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Taniguchi, H., Lu, J. & Huang, Z. J. The spatial and temporal origin of chandelier cells in mouse neocortex. Science 339, 70–74 (2013).

    Article  CAS  PubMed  Google Scholar 

  64. Mi, D. et al. Early emergence of cortical interneuron diversity in the mouse embryo. Science 360, 81–85 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Hu, H., Gan, J. & Jonas, P. Interneurons. fast-spiking, parvalbumin(+) GABAergic interneurons: from cellular design to microcircuit function. Science 345, 1255263 (2014).

    Article  PubMed  CAS  Google Scholar 

  67. Seung, H. S. & Sumbul, U. Neuronal cell types and connectivity: lessons from the retina. Neuron 83, 1262–1272 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sabbagh, M. F. et al. Transcriptional and epigenomic landscapes of CNS and non-CNS vascular endothelial cells. eLife 7, e36187 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Hobert, O., Carrera, I. & Stefanakis, N. The molecular and gene regulatory signature of a neuron. Trends Neurosci. 33, 435–445 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Thiebaud, P. et al. Intersecting batteries of differentially expressed genes in the early sea urchin embryo. Genes Dev. 4, 1999–2010 (1990).

    Article  CAS  PubMed  Google Scholar 

  76. Hodge, R. D. et al. Conserved cell types with divergent features between human and mouse cortex. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/384826v1 (2018).

  77. Tosches, M. A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles. Science 360, 881–888 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. Arendt, D. et al. The origin and evolution of cell types. Nat. Rev. Genet. 17, 744–757 (2016).

    Article  CAS  PubMed  Google Scholar 

  79. Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Quadrato, G. et al. Cell diversity and network dynamics in photosensitive human brain organoids. Nature 545, 48–53 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Tsunemoto, R. et al. Diverse reprogramming codes for neuronal identity. Nature 557, 375–380 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Enwright Iii, J. F. et al. Transcriptome alterations of prefrontal cortical parvalbumin neurons in schizophrenia. Mol. Psychiatry 23, 1606–1613 (2018).

    Article  CAS  PubMed  Google Scholar 

  83. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. He, M. & Huang, Z. J. Genetic approaches to access cell types in mammalian nervous systems. Curr. Opin. Neurobiol. 50, 109–118 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Schwarz, L. A. et al. Viral-genetic tracing of the input-output organization of a central noradrenaline circuit. Nature 524, 88–92 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  CAS  PubMed  Google Scholar 

  88. Coskun, A. F. & Cai, L. Dense transcript profiling in single cells by image correlation decoding. Nat. Methods 13, 657–660 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank B. Mensh, A. Kepecs, L. Luo and P. Mitra for critical reading and comments of the manuscript. Z.J.H. is supported by the US National Institutes of Health (NIH) U19MH114823-01, 5R01MH109665-02 and 5R01MH101268-05 and the Robertson Neuroscience Fund at Cold Spring Harbor Laboratory. A.P. is supported by a NARSAD Young Investigator Award from the Brain & Behavior Research Foundation.

Author information

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, New York, NY, USA

    Z. Josh Huang & Anirban Paul

  2. Department of Neural & Behavioral Sciences, Penn State University College of Medicine, Hershey, PA, USA

    Anirban Paul

Authors
  1. Z. Josh Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anirban Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors researched data for the article and contributed substantially to the discussion of content. Z.J.H. wrote the article assisted by A.P. Both authors reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Z. Josh Huang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Z.J., Paul, A. The diversity of GABAergic neurons and neural communication elements. Nat Rev Neurosci 20, 563–572 (2019). https://doi.org/10.1038/s41583-019-0195-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41583-019-0195-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing